class_docstr: Base class for detectors.
docstr: Extract features from multiple images.
  
  Args:
      imgs (list[torch.Tensor]): A list of images. The images are
          augmented from the same image but in different ways.
  
  Returns:
      list[torch.Tensor]: Features of different images
docstr_tok: ["Extract","features","from","multiple","images",".","Args",":","imgs","(","list","[","torch.Tensor","]",")",":","A","list","of","images",".","The","images","are","augmented","from","the","same","image","but","in","different","ways",".","Returns",":","list","[","torch.Tensor","]",":","Features","of","different","images"]
code: def extract_feats(self, imgs):
      """Extract features from multiple images.
  
      Args:
          imgs (list[torch.Tensor]): A list of images. The images are
              augmented from the same image but in different ways.
  
      Returns:
          list[torch.Tensor]: Features of different images
      """
      assert isinstance(imgs, list)
      return [self.extract_feat(img) for img in imgs]
  
imports: from abc import ABCMeta, abstractmethod
  from collections import OrderedDict
  import mmcv
  import numpy
  import torch
  import torch.distributed
  import torch.nn
  from mmcv.runner import auto_fp16
  from mmcv.utils import print_log
  from mmdet.utils import get_root_logger
label_desc: Use image node_id 7 for calling the BaseDetector obj's underlying member method code with example usage: obj.extract_feats(imgs) and returns: unknown